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Harvard University

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Not worth the easy work schedule - Anonymous employee Harvard University Employee Review

2.0
Dec 30, 2015
Anonymous employee
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Pros

Subject matter can be interesting; easy hours; everyone gets a week off between Christmas and New Years; your relatives back home in Indiana will be impressed that you're working at Harvard.

Cons

Uncompetitive salaries and benefits; no equity; no career opportunities for most fields; highly de-centralized structure: If your boss is crazy or abusive there is generally nothing you can do about it (Harvard rewards tenure with management positions, thus managers are often untrained, incompetent, and unfit to manage anyone.) Generally dysfunctional and infantile atmosphere (not uncommon at higher education institutions). During the recession Harvard chose to lay off long-term employees rather than dip into their $20B. At no time did President Faust ever officially address her employees. It was an unconscionable choice.

Explore other reviews about Harvard University

5.0
Jun 25, 2026
Recommend
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Pros

A PhD is always very advisor/PI dependent, but I had a great experience with room to learn, grow, and explore interesting research questions. As with any PhD program you’ll work pretty hard for relatively lower pay and perhaps less directly applicable industry career trajectory at the end, but if you find good people to tackle the journey with and get to work on interesting problems I personally think the journey is worthwhile!

Cons

Less of a program wide cohort in my particular engineering field. Some funding challenges with the govt last year but seem mostly back now, apart from the recent administration issues funding wasn’t generally a challenge

5.0
Jun 3, 2026
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Business outlook

Pros

You work with experts from every field possible in science from Mechanobiology to Metallurgy and working as a ML intern along with scientists from different fields gave me a lot of exposure and how to work in a research department.

Cons

One challenge encountered was maintaining a clear focus on training objectives, as the evolving parameters required continuous reassessment of the model's learning priorities. Additionally, the absence of established ground truth for the domain presented a limitation in validating and guiding the approach effectively.

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